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Knowledge Graphs (KGs) have recently gained attention for representing knowledge about a particular domain. Since its advent, the Linked Open Data (LOD) cloud has constantly been growing containing many KGs about many different domains such as ...

Social media platforms have been growing at a rapid pace, attracting users engagement with contents due to their convenience facilitated by many usable features. Such platforms provide users with interactive options such as likes, dislikes as well as a ...

Drug development is a costly and time consuming activity. The traditional process relies on extensive experimental efforts to map out the relevant part of the chemical space. Data about molecules, diseases, genes and other entities are present on many ...

Access to medical data is highly regulated due to its sensitive nature, which can constrain communities' ability to utilise these data for research or clinical purposes. Common de-identification techniques to enable the sharing of data may not provide ...

Storytelling is an ancient art and science of conveying wisdom through generations for centuries. Data-driven storytelling in the context of a natural language corpus has a huge potential for conveying fast valuable insights about the corpus for better ...

The ultimate goal of my long-term project is "Augmented Inventing." This work is a follow-up effort toward the goal. It leverages the structural metadata in patent documents and the text-to-text mappings between metadata. The structural metadata ...

Despite the efforts to regulate privacy policies to protect user privacy, these policies remain lengthy and hard to comprehend. Powered by machine learning, our publicly available browser extension, PrivacyCheck v2, automatically summarizes any privacy ...

Automatically generating or ranking distractors for multiple-choice questions (MCQs) is still a challenging problem. In this work, we have focused towards automatic ranking of distractors for MCQs. Accordingly, we have proposed an semantically aware CNN-...

Predicting endings for narrative stories is a grand challenge for machine commonsense reasoning. The task requires ac- curate representation of the story semantics and structured logic knowledge. Pre-trained language models, such as BERT, made progress ...

An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a bootstrapping manner. While standard AL heuristics, such as selecting those points for annotation for which a classification ...

In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leveraging a network of two capsule layers. Caps2NE induces a routing process to ...

Semi-supervised consensus clustering integrates supervised information into consensus clustering in order to improve the quality of clustering. In this paper, we study the novel Semi-MultiCons semi-supervised consensus clustering method extending the ...

Building a robust predictive model requires an array of steps such as data imputation, feature transformations, estimator selection, hyper-parameter search, ensemble construction, amongst others. Due to this vast, complex and heterogeneous space of ...

A practical large scale product recognition system suffers from the phenomenon of long-tailed imbalanced training data under the E-commercial circumstance at Alibaba. In addition to images of products at Alibaba, plenty of related side information (e.g. ...

Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existing scientific literature search systems provide text search capabilities and can identify similar papers, gaining an in-depth understanding of a new approach ...

Machine learning (ML) based data analysis has attracted an increasing attention in the manufacturing industry, however, many challenges hamper their wide spread adoption. The main challenges are the high costs of labour-intensive data preparation from ...

Information extraction is a well-known topic that plays a critical role in many NLP applications as its outputs can be considered as an entrance step for digital transformation. However, there still exist gaps when applying research results to actual ...

Despite the great success of Graph Machine Learning (GML) in a variety of applications, the industry is still seeking a platform which makes performing industrial-purpose GML convenient. In this demo, we present EasyGML, a fully-functional and easy-to-...

With the rapid growth of data volume, data-driven machine learning models have become a necessary part of many industrial applications. Intuitively, the more high-quality data used for training leads to better model performance. However, in reality, ...

Quasimodo is an open-source commonsense knowledge base that significantly advanced the state of salient commonsense knowledge base construction. It introduced a pipeline that gathers, normalizes, validates and scores statements coming from query log and ...